This session explores deploying ONNX machine learning models via WebAssembly on lightweight k0s Kubernetes clusters—enabling fast, secure AI inference in resource-constrained environments. The talk demonstrates running pre-trained models from frameworks like PyTorch and TensorFlow through ONNX, executed inside WebAssembly’s sandboxed runtime on minimal Kubernetes.

Topics include:
– ONNX Runtime with WebAssembly on k0s
– Using WASI to access and run models
– Optimizing performance for Wasm-based inference
– Model loading, caching, and scaling strategies
– CI/CD integration for Wasm ML pipelines

Attendees will see real-world benchmarks comparing Wasm-based vs. containerized inference, focusing on latency, memory usage, cold start, and throughput. This approach reduces infrastructure cost, improves isolation, and unlocks edge AI use cases where traditional containers fall short.